Conditional distributions for the nested Dirichlet process via sequential imputation

We consider an array of random variables, taking values in a complete and separable metric space, that exhibits a kind of symmetry which we call row exchangeability. Given such an array, a natural model for Bayesian nonparametric inference is the nested Dirichlet process (NDP). Exactly determining posterior distributions for the NDP is infeasible, since the computations involved grow exponentially with the sample size. In this paper, we present a new approach to determining these posterior distributions that involves the use of sequential